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- Prediction Profiler for Generalized Linear Models with Confidence Limits Formula

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Status:
Open for Voting
Submitted by
jszarka
on
06-26-2018
11:08 AM

When I am using the Fit Model platform with Standard Least Squares, I can effectively save the prediction formula and standard errors to a separate column. When I do this for multiple responses, I can effectively use the Graph -> Profiler menu to overlay those models. This is very helpful, as each response may have a different set of meaningful predictors in it. Here is a reference to such a thread on the topic:

https://community.jmp.com/t5/Discussions/Confidence-Interval-in-prediction-profiler/td-p/10817

However, if I have a generalized linear model (GLM) such as a logistic regression, I have no such luck. Within the single model for fitting the GLM Personality, the results in that platform have confidence intervals on them. However, I am only able to export the prediction formula. If my most 2 important responses contains one normal response with Standard Least Squares, and one binomial response with a GLM logistic regression model, I cannot create a joint Prediction Profiler with effective predictions and confidence intervals. I am optimistic this can be done because we already have evidence that the confidence intervals can be created for GLMs. You just have to work out what the StdErr Pred Formula column would look like.

I brought this up as a ticket with JMP as well. The following series of responses from JMP support certainly brought attention to the issue, but perhaps if others find value in this enhancement, then it is more likely to be completed.

*"Unfortunately, at the moment, I do not see a way to get a confidence interval around the predicted probability for the binary response in the combined standalone profiler."*

*"I see what you mean about the inability to save Standard Error Formulas for GLM's or for Least Squares models containing nested terms. I am not aware of any particular reason why this is the case. I will pass these items on to JMP R&D for their consideration in a future version of JMP."*

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